Net passenger flux at high-speed railway stations between Nanjing and Shanghai from August 14 to September 3, 2016.

<p>Positive flux value represents outflow > inflow, and vice versa.</p

Time series of daily occupancy rates for the two intercity trains G9001 and G9002 between Langfang and Beijing during March 31—April 22, 2015.

<p>The horizontal color bands indicate mean occupancy rates with bandwidths indicating standard errors. The gray shaded areas indicate weekdays from Monday to Thursday.</p

Exploring the potential of open big data from ticketing websites to characterize travel patterns within the Chinese high-speed rail system

<div><p>Big data have contributed to deepen our understanding in regards to many human systems, particularly human mobility patterns and the structure and functioning of transportation systems. Resonating the recent call for ‘open big data,’ big data from various sources on a range of scales have become increasingly accessible to the public. However, open big data relevant to travelers within public transit tools remain scarce, hindering any further in-depth study on human mobility patterns. Here, we explore ticketing-website derived data that are publically available but have been largely neglected. We demonstrate the power, potential and limitations of this open big data, using the Chinese high-speed rail (HSR) system as an example. Using an application programming interface, we automatically collected the data on the remaining tickets (RTD) for scheduled trains at the last second before departure in order to retrieve information on unused transit capacity, occupancy rate of trains, and passenger flux at stations. We show that this information is highly useful in characterizing the spatiotemporal patterns of traveling behaviors on the Chinese HSR, such as weekend traveling behavior, imbalanced commuting behavior, and station functionality. Our work facilitates the understanding of human traveling patterns along the Chinese HSR, and the functionality of the largest HSR system in the world. We expect our work to attract attention regarding this unique open big data source for the study of analogous transportation systems.</p></div

Locations of high-speed railway stations between Nanjing and Shanghai.

<p>Locations of high-speed railway stations between Nanjing and Shanghai.</p

Between-station correlation matrix of passenger flux between Nanjing and Shanghai from August 14 to September 3, 2016.

<p>The Pearson’s correlation coefficients are shown.</p

Retrieval of net passenger flux at stations using remaining ticket data.

<p>For a given scheduled train passing Station A, B and C sequentially, by enquiring into the numbers of remaining tickets for trips from Station A to B (n₁), and B to C (n₂), the net flux at station B can be calculated as their difference (Δn = n₁—n₂). Δn>0 represents outflow > inflow at station B, and vice versa.</p

Time series of passenger flux at stations between Nanjing and Shanghai from August 14 to September 3, 2016.

<p>Each color curve represents a single station. Positive flux value represents outflow > inflow, and vice versa.</p

Time series of hourly remaining ticket data for all high-speed trains from Nanjing to Shanghai from November 22—December 26, 2016.

<p>Each curve represents a single day.</p